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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses.

Franz Faul1, Edgar Erdfelder, Axel Buchner

  • 1Christian-Albrechts-Universität, Kiel, Germany. ffaul@psychologie.uni-kiel.de

Behavior Research Methods
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PubMed
Summary
This summary is machine-generated.

G*Power software now includes advanced power analysis for correlation and regression. This free tool enhances statistical testing capabilities for researchers in various fields.

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Area of Science:

  • Statistics
  • Statistical Software
  • Quantitative Research Methods

Background:

  • G*Power is a widely used free program for statistical power analysis.
  • Previous versions provided power analysis for numerous statistical tests.
  • Enhancements were needed for correlation and regression analyses.

Purpose of the Study:

  • To introduce extensions and improvements to the G*Power program.
  • To add new procedures for power analysis in correlation and regression.
  • To describe the scope and handling of these new features.

Main Methods:

  • Implementation of new power analysis procedures within G*Power.
  • Focus on single-sample tetrachoric correlations.
  • Inclusion of comparisons of dependent correlations.
  • Addition of bivariate and multiple linear regression analyses (random predictor model).
  • Integration of logistic and Poisson regression power analyses.

Main Results:

  • New G*Power version offers enhanced power analysis for specific correlation types.
  • Expanded capabilities include various regression models.
  • The updated software facilitates more precise statistical planning.

Conclusions:

  • The enhanced G*Power program provides researchers with expanded tools for power analysis in correlation and regression.
  • These updates support more robust study design and interpretation.
  • The free accessibility of G*Power ensures broad utility for the scientific community.